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Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
Yan-Bo Lin · Hung-Yu Tseng · Hsin-Ying Lee · Yen-Yu Lin · Ming-Hsuan Yang

Thu Dec 09 08:30 AM -- 10:00 AM (PST) @ None #None

The audio-visual video parsing task aims to temporally parse a video into audio or visual event categories. However, it is labor intensive to temporally annotate audio and visual events and thus hampers the learning of a parsing model. To this end, we propose to explore additional cross-video and cross-modality supervisory signals to facilitate weakly-supervised audio-visual video parsing. The proposed method exploits both the common and diverse event semantics across videos to identify audio or visual events. In addition, our method explores event co-occurrence across audio, visual, and audio-visual streams. We leverage the explored cross-modality co-occurrence to localize segments of target events while excluding irrelevant ones. The discovered supervisory signals across different videos and modalities can greatly facilitate the training with only video-level annotations. Quantitative and qualitative results demonstrate that the proposed method performs favorably against existing methods on weakly-supervised audio-visual video parsing.

Author Information

Yan-Bo Lin (University of North Carolina at Chapel Hill)
Hung-Yu Tseng (Facebook)
Hsin-Ying Lee (University of California, Merced)
Yen-Yu Lin (National Chiao Tung University)
Ming-Hsuan Yang (Google / UC Merced)

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